Analytical Model for Progressive Collapse of RC Frame Beam-Column Substructures Using Multi-Gene Genetic Programming
Kaiqi Lin, Daoyuan Li, Linlin Xie, Min He, Ying Sun
Abstract
Establishing a concise and accurate analytical model is the key to developing a feasible progressive collapse design for engineering practice. However, existing models either focused on an individual force mechanism or required complicated computer programming. Among existing machine learning (ML) techniques, multi-gene genetic programming (MGGP) can be trained to obtain explicit formulas for engineering problems. In this study, a comprehensive database was established by data collection, Latin hypercube sampling and structural design, and was used to train the mathematical model for quantifying progressive collapse resistance of reinforced concrete (RC) beam-column substructures under middle column removal scenarios. Further, an energy-based error index was proposed to validate the accuracy of the MGGP model among others. The research outcomes can provide references for the development of simplified analytical models for calculating the progressive collapse progress of RC frame structures, and promote the development of the practical design method.